Heterogeneous Network (HetNet) is a strategy introduced in LTE-A with the purpose of improving network capacity. In a HetNet depicted in FIG. 1 many pico-cells with low transmit power (˜250 mW) overlay on a macro-cell with higher transmit power (1-40 W). The advantage of this layout is to perform off-loading macro-cell data traffic to smaller pico-cells and thus improving network capacity. Moreover, these cells are using the same frequency resources implying that a receiver node can suffer high interference from macro-cell(s) leading to performance degradation. Therefore, interference management is a crucial aspect in any HetNet.
LTE Release 10 has adopted enhanced-inter-cell interference coordination (e-ICIC) as part of the interference management. One key feature in e-ICIC is time domain interference management or also known as almost blank sub-frame (ABS) transmission. An ABS contains common reference symbols (CRS), broadcast channel (BCH), and synchronization signals (PSS/SSS). As shown in FIG. 1, while the UE in cell range expansion maintains data transmission to a pico-cell (serving cell), the macro cell (neighbor cell) transmits an ABS sub-frame. Depending on the cell-IDs, the CRS of the neighbor cell can be colliding with CRS of a serving cell (known as colliding CRS scenario) or it can be colliding with the data/control channels of the serving cell (known as non-colliding CRS scenario). Thus, in a non-colliding CRS scenario, the interference level of the control/data channel of the downlink serving cell is limited to the CRS from macro/neighbor cell(s). To combat the inter-cell interference at the receiver some prior art techniques have been proposed.
Successive interference cancellation (SIC) is a well-known technique within the CRS interference cancellation (CRS-IC) area. SIC performs interference cancellation from dominant interferences (often neighbor cells) successively. The typical operation at a receiver node, such as a UE, with CRS-SIC is as follows:                Estimating the channel frequency response (CFR) of the first dominant neighbor cell;        Creating a replica of the dominant interference by multiplying the locally generated CRS of neighbor cell with the estimated CFR;        Removing the dominant interference by subtract the received signal with the re-created/replica of the dominant interference;        Repeating the previous steps whenever there is a need to cancel the second or subsequent dominant interference; and        Once the interference(s) have been removed, the receiver node continues to perform demodulation of desired signal from serving cell.        
The SIC technique, however, has at least the following technical issues:                The interference cancellation is depending on the accuracy of the neighbor cell(s) channel estimation.        The error in the first cancellation is propagated to the subsequent cancellation and thus increasing the residual error. Hence, the error propagation leads to performance degradation.        Implementation complexity and latency are expected to be linearly increasing with the number of cell(s) to be cancelled.        
Another prior art solution is so called log-likelihood ratio (LLR) muting at the receiver. LLR muting attenuates the set of LLRs corresponding to the interfered data symbols, or in some cases, even sets them to zero. This method requires only a negligible computational effort at the receiver, but yields only minor gains.
Yet another prior art solution is the robust equalizer (RBE). In the RBE, the interference from the neighbor cell(s) is treated as Gaussian noise with a known correlation matrix. The correlation matrix is known since it is assumed that it follows the same distribution as the serving cell channel. Thereafter the RBE performs minimum mean square error (MMSE) equalization where the correlation of the interference is appropriately treated.
The technical drawback of the RBE is that it is limited to the MMSE detection which is significantly worse in performance than near optimum detectors, such as maximum-likelihood (ML) detection.